The ENM Statistical Difference Workflow (ESW DIFF) allows the computation of the extent and intensity of change in species potential distribution through computation of the differences between two raster layers using the R statistical environment (R Core Team 2013). The difference file is computed from two input files (in this case present projection and 2050 projection) coming from the Ecological Niche Modelling (ENM) Workflow (http://www.myexperiment.org/workflows/3355).
The difference bet...

The ENM Statistical Stack Workflow (ESW STACK) allows the computation of the extent, intensity and a cummulated potential species distribution through computation of an average sum layer from the input raster layers using the R statistical environment (R Core Team 2013). The sum layer is computed from all input files. e.g from different distribution of species as a mean value from each corresponding raster cell values, coming from the Ecological Niche Modelling (ENM) Workflow (http://www.myex...

Biome-BGC is a process-based biogeochemical model that can be used to simulate carbon, nitrogen and water fluxes of different terrestrial ecosystems. The model can help us to quantify a broad range of ecosystem service indicators. These newly developed measures include: annual wood increment, yearly production of grasslands or croplands, total average carbon stock, annual evapotranspiration, damping of ecosystem daily water outflow, living and dead biomass protecting the soil against erosion,...

Biome-BGC is a process-based biogeochemical model that can be used to simulate carbon, nitrogen and water fluxes of different terrestrial ecosystems. A new version of the model, called Biome-BGC MuSo was developed to perform more realistic simulations in terms of soil hydrology, and improved ecosystem management options essentially (Hidy et al. 2012; Hidy & Barcza 2014). The Biome-BGC CARBON service executes a single simulation run at a given geographic location under that distinctive environ...

Biome-BGC is a process-based biogeochemical model that can be used to simulate carbon, nitrogen and water fluxes of different terrestrial ecosystems. Two models have been implemented: the Biome-BGC v4.1.1 Max Planck Institute model, and the newly developed Biome-BGC MuSo 3.0 model. Performance, success or failure of these models are highly dependent on parameter settings and variation. Due to the high number of parameters (around 40 and 60 for 4.1.1 MPI and MuSo respectively) and the non-line...

Biome-BGC is working with a lots of ‘a priori’ unknown and hard to obtain model parameters. Therefore the parameterization is a critical step of using the model. Parameteres can be estimated using inverse calibration techniques based on measurement data, which means that the model is being calibrated. Measurement data have to be collected with respect to the model in order to compare them. Comparison is based on misfit measure (e.g. a sort of likelihood value), which is the function of the di...

This workflow must run after the workflow: Resident killer whale-chinook salmon interactions. The interaction workflows generates a PostWorkspace file, this is a zip file and it is an R Workspace that transfers values from the Resident killer whale-chinook salmon interactions (main) workflow to the Exploration of fishing scenario (post-processing) workflow.
This workflow merges statistical inference derived from linkages between RKW vital rates (survival probability and fecundity rates) a...

The resident killer whale-chinook salmon interactions workflow provides an environment to calculate a two-sex stage-structured matrix with no density dependence and with vital rates as random variables or as functions of Chinook abundance from specific stock aggregates and to (i) quantify the differences in demographic rates between killer whale (Orcinus orca) population that explain population growth; (ii) to determine the relative influence of vital rates and Chinook (Oncorhynchus tshawytsc...